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Glaucomatous vision loss may be preceded by an enlargement of the cup\u2010to\u2010disc ratio (CDR). We propose to develop and validate an artificial\u2010intelligence\u2010based CDR grading system that may aid in effective glaucoma\u2010suspect screening.\n                    <jats:italic>Design, Setting, and Participants<\/jats:italic>\n                    . 1546 disc\u2010centered fundus images were selected, including all 457 images from the Retinal Image Database for Optic Nerve Evaluation dataset, and images were randomly selected from the Age\u2010Related Eye Disease Study and Singapore Malay Eye Study to develop the system. First, a proprietary semiautomated software was used by an expert grader to quantify vertical CDR. Then, using CDR below 0.5 (nonsuspect) and CDR above 0.5 (glaucoma suspect), deep\u2010learning architectures were used to train and test a binary classifier system.\n                    <jats:italic>Measurements<\/jats:italic>\n                    . The binary classifier, with glaucoma suspect as positive, is measured using sensitivity, specificity, accuracy, and AUC.\n                    <jats:italic>Results<\/jats:italic>\n                    . The system achieved an accuracy of 89.67% (sensitivity, 83.33%; specificity, 93.89%; and AUC, 0.93). For external validation, the Retinal Fundus Image Database for Glaucoma Analysis dataset, which has 638 gradable quality images, was used. Here, the model achieved an accuracy of 83.54% (sensitivity, 80.11%; specificity, 84.96%; and AUC, 0.85).\n                    <jats:italic>Conclusions<\/jats:italic>\n                    . Having demonstrated an accurate and fully automated glaucoma\u2010suspect screening system that can be deployed on telemedicine platforms, we plan prospective trials to determine the feasibility of the system in primary\u2010care settings.\n                  <\/jats:p>","DOI":"10.1155\/2021\/6694784","type":"journal-article","created":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T14:05:50Z","timestamp":1622297150000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["An Artificial\u2010Intelligence\u2010 and Telemedicine\u2010Based Screening Tool to Identify Glaucoma Suspects from Color Fundus Imaging"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5775-2228","authenticated-orcid":false,"given":"Alauddin","family":"Bhuiyan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Govindaiah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. 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